This paper aims to propose and illustrate an approach for estimating the effect of exposures measurements, which are taken from both monitors and high-resolution grid-level data, on health outcomes measured as counts on irregular blocks. The estimation method performs data fusion and also accounts for the measurement error in the monitors and in the high-resolution proxy data using an approach derived from the Bayesian melding model. The models are latent Gaussian and are estimated using a two-stage approach using the integrated nested Laplace approximation (INLA). Moreover, the stochastic partial differential equation approach is used to efficiently estimate the spatial field [1]}, [2]}.
